摘要
为了能够建立精确的沥青混合料细观力学模型、优化混合料性能,需要先构建具有真实形态学特征的集料模型并计算分析集料形态参数,利用3D激光扫描技术采集了辉绿岩、石灰岩、玄武岩与卵石4种集料共500颗样本的三维点云数据,通过球谐函数简化集料模型,建立了三维集料样本库。归纳总结了现有集料的形态指标,通过皮尔逊相关性分析,选取F_(I)、A_(I)定量表征粗集料三维形态特征,对不同种类以及粒径集料的F_(I)、A_(I)进行统计分析。研究结果显示:当球谐序列阶数为10,三角形映射网格数为643时,简化后集料模型基本形状、体积与表面积均与真实集料非常接近;集料的F_(I)、A_(I)服从正态分布与对数正态分布,且随集料类型和粒径的变化而变化。
To establish an accurate fine mechanical model of asphalt mixtures and optimize the performance of mixtures,it is necessary to construct an aggregate model with real morphological characteristics and calculate and analyze the morphological parameters of aggregates.In this paper,the three-dimensional(3D)laser scanning technology was used to collect the 3D point cloud data of 500 samples of four kinds of aggregates,namely pyroxene,limestone,basalt and pebble,to simplify the aggregate model through the spherical harmonic function,and to establish a 3D aggregate sample library.The morphology indexes of existing aggregates were summarized,and F_(I) and A_(I) were selected to quantitatively characterize the 3D morphology of coarse aggregates through Pearson correlation analysis,and F_(I) and A_(I) were statistically analyzed for different types and grain sizes of aggregates.Results show that the basic shape,volume and surface area of the simplified aggregate model are very close to those of the real aggregates when the order of the spherical harmonic series is 10 and the number of triangular mapping grids is 643;the F_(I) and A_(I) of aggregates obey the normal and lognormal distributions,and varies with aggregate types and grain sizes.
作者
汤文
谢明杰
黄丹
TANG Wen;XIE Mingjie;HUANG Dan(School of Automotive and Traffic Engineering,Wuhan University of Science and Technology,Wuhan 430065,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2024年第8期948-957,共10页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(51508428)
青海省重点研发与转化计划项目科技援青合作专项资助项目(2021-QY-207)
青海省交通运输厅科技资助项目(2022-01)。
关键词
沥青混合料
粗集料
三维激光扫描
球谐函数
形态特征
皮尔逊相关系数
asphalt mixture
coarse aggregate
3D laser scanning
spherical harmonics
morphological features
Pearson correlation coefficient